Bias Attributable to Composite Outcome
AbstractBackgroundLittle guidance is available on how composite outcomes should be interpreted, especially in situations of varied direction in the association across the event subtypes. I proposed an index to evaluate the bias attributable to composite outcomes (BACO) and applied it in recently published clinical trials.MethodsI defined the BACO index as the ratio between logarithms of the association measures of both a composite outcome and its most relevant component (e.g., any-cause mortality). By using the non-linear combination of parameters, based on the delta method, I calculated the confidence intervals and performed Wald-type tests for the null hypotheses (BACO index = 1). I applied this method in systematically selected clinical trials, and in two other preselected trials which I considered “positive controls”. These last trials have been recognized as examples of primary composite outcomes that were disregarded because of inconsistency with the treatment effect on mortality.ResultsBACO index values different from one were classified according to whether the use of composite outcomes overestimated (BACO index >1), underestimated (BACO index between zero and <1), or inverted (BACO index <0) the association between exposure and prognosis. In three of 23 clinical trials and the two positive controls, the BACO indices were significantly lower than one (using p <0.005 as a preset cutoff).ConclusionBased on the BACO index testing, researchers could predefined rules to make impartial decisions about maintaining a composite outcome as the primary endpoint or to state cautions regarding its interpretation.Key MessagesDiscrepancies between the effects on composite outcomes and those on their most critical components make the interpretation of research results a challenge.An index based on the ratio of association measures can be used to evaluate the correspondence between the composite outcome and its most critical component.This index could help to preset rules to make decisions for interpretation of clinical studies.